TL;DR We’ll:
Learn an interesting method for generalizing inferences from a biased sample to a population of interest See why prior predictive checks are great Implement a simple mixed-effects model in TFP Intro This post is a TFP port of Lauren Kennedy and Jonah Gabry’s excellent MRP with rstanarm vignette. It describes a very interesting statistical method for generalizing inferences from a biased sample to a population of interest.

TL;DR Covariance matrices allow us to capture parameter correlations in multivariate hierarchical models; sampling these using Hamiltonian Monte Carlo in Tensorflow Probability can be tricky and confusing; this post is about some of the math involved and how to get this right.
Intro Hierarchical models allow us to account for variations between different groups in our data. Let’s say that, for some reason, we have different groups of tadpoles in different tanks and we want to model per-tank survival rates.

Intro This post is about building varying intercepts models using TensorFlow Probability (“TFP”). It’s basically my attempt to translate Sigrid Keydana’s wonderful blog post from R to Python. I’m doing this for a couple of reasons: First, I’ve played with TFP before, was quite impressed by its performance and flexibility, and wanted to learn more about it; Second, I wanted to start blogging, and this seemed like an easy start; Last, TFP is rather new, and there aren’t a whole lot of resources and tutorials about it - so this might even prove useful to someone, someday.

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